Stereology in the Era of Digital Histopathology and Artificial Intelligence

Güneş A.

5th Asian and African Stereology Congress, İstanbul, Türkiye, 26 - 27 Haziran 2026, ss.1-2, (Özet Bildiri)

  • Yayın Türü: Bildiri / Özet Bildiri
  • Basıldığı Şehir: İstanbul
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.1-2
  • İstanbul Medipol Üniversitesi Adresli: Evet

Özet

Artificial intelligence (AI) is becoming increasingly integrated into biomedical research and pathology. Advances in digital pathology, high-resolution imaging, and data storage technologies have enabled the digitisation, sharing, and remote evaluation of histological slides, improving collaboration among histopathologists. Digital pathology has also provided the foundation for AI-based image analysis tools. Machine learning and deep learning algorithms are being applied to histopathological images for tissue segmentation, cell detection, classification, and quantitative analysis. Although many of these technologies are still undergoing validation, their use in pathology research continues to expand. AI performance improves through training on large datasets and expert feedback; however, the reasoning behind AI-generated decisions is often not fully transparent, a limitation commonly referred to as the “black box” problem. Stereology is a design-based quantitative science that enables unbiased three-dimensional estimation from two-dimensional tissue sections through systematic sampling and mathematical principles. In this context, AI has the potential to enhance stereological workflows by automating data extraction, accelerating image analysis, improving reproducibility, and reducing observer-dependent variability. Nevertheless, AI-based analyses may be affected by image quality, dataset composition, computational constraints, and bias introduced during model training. In conclusion, advances in digital pathology and AI are creating new opportunities for stereological research. The successful integration of AI into stereological workflows requires careful validation and adherence to established stereological principles. The future of stereological research will likely depend on combining the efficiency of AI with the methodological rigour of stereology.

Keywords: Artificial intelligence, digital pathology, stereology, quantitative histopathology, machine learning, image analysis.

References:

Kaplan S, editor. A Manual of the Stereological Techniques. New York: Nova Science Publishers; 2024. DOI: 10.52305/EIGK1265

Mühlfeld C. Stereology and three-dimensional reconstructions to analyze the pulmonary vasculature. Histochem Cell Biol. 2021 Aug;156(2):83-93. doi: 10.1007/s00418-021-02013-9. Epub 2021 Jul 16. PMID: 34272602; PMCID: PMC8397636.

Transforming precision medicine powered by AI-pathology. Nucleai. Accessed June 19, 2026. https://nucleai.ai/

Zhang DY, Venkat A, Khasawneh H, Sali R, Zhang V, Pei Z. Implementation of Digital Pathology and Artificial Intelligence in Routine Pathology Practice. Lab Invest. 2024 Sep;104(9):102111. doi: 10.1016/j.labinv.2024.102111. Epub 2024 Jul 23. PMID: 39053633.